Bibliographic record
Abstract
A tailored transfusion algorithm based on viscoelastic testing in the perioperative period or in trauma patients is recommended by guidelines for bleeding management. Bleeding management strategies in neonates and children are mostly extrapolated from the adult experience, as published evidence in the youngest age group is scarce. This manuscript is intended to give a structured overview of what has been published on the use of viscoelastic testing to guide bleeding management in neonates and children. Several devices that use either the traditional viscoelastic method or resonance viscoelastography technology are on the market. Reference ranges for children have been evaluated in only some of them. As most of the hemostasis maturation processes can be observed during the first year of life, adult reference ranges for viscoelastic testing could be applied over the age of 1 year. The majority of the published trials in children are based on retrospective analyses describing the correlation between viscoelastic testing and standard laboratory testing or focusing on the prediction of bleeding. Clinically more relevant studies in pediatric patients undergoing cardiac surgery have demonstrated that the implementation of a transfusion algorithm based on viscoelastic testing has significantly reduced transfusion requirements and that this approach has enabled a rapid detection of coagulation disorders in the presence of excessive bleeding. Although further studies are urgently needed, experts have reviewed the use of a transfusion algorithm based on viscoelastic testing in children as a feasible approach, as it has been shown to improve bleeding management and rationalize blood product transfusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".